T
Tatyana Danilov
Researcher at Icahn School of Medicine at Mount Sinai
Publications - 3
Citations - 332
Tatyana Danilov is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Deep learning & Internal medicine. The author has an hindex of 1, co-authored 3 publications receiving 143 citations.
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Journal ArticleDOI
Characterization of Myocardial Injury in Patients With COVID-19.
Gennaro Giustino,Lori B. Croft,Giulio G. Stefanini,Renato Bragato,Jeffrey J. Silbiger,Marco Vicenzi,Tatyana Danilov,Nina Kukar,Nada Shaban,Annapoorna Kini,Anton Camaj,Solomon Bienstock,Eman Rashed,Karishma Rahman,Connor P. Oates,Samantha Buckley,Lindsay Elbaum,Derya Arkonac,Ryan Fiter,Ranbir Singh,Emily Li,Victor Razuk,Sam E. Robinson,Michael L. Miller,Benjamin Bier,Valeria Donghi,Marco Pisaniello,Riccardo Mantovani,Giuseppe Pinto,Irene Rota,Sara Baggio,Mauro Chiarito,Fabio Fazzari,Ignazio Cusmano,Mirko Curzi,Richard Ro,Waqas Malick,Mazullah Kamran,Roopa Kohli-Seth,Adel Bassily-Marcus,Eric Neibart,Gregory Serrao,Gila Perk,Donna M. Mancini,Vivek Y. Reddy,Sean Pinney,George Dangas,Francesco Blasi,Samin K. Sharma,Roxana Mehran,Gianluigi Condorelli,Gregg W. Stone,Valentin Fuster,Stamatios Lerakis,Martin E. Goldman +54 more
TL;DR: An international, multicenter cohort study including 7 hospitals in New York City and Milan of hospitalized patients with laboratory-confirmed COVID-19 who had undergone transthoracic echocardiographic (TTE) and electrocardiographic evaluation during their index hospitalization found cardiac structural abnormalities were present in nearly two-thirds of patients with myocardial injury.
Posted ContentDOI
Using deep learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram.
Akhil Vaid,Kipp W. Johnson,Marcus A. Badgeley,Sulaiman Somani,Mesude Bicak,Isotta Landi,Adam Russak,Shan Zhao,Matthew A. Levin,Robert S. Freeman,Alexander W. Charney,Atul Kukar,Bette Kim,Tatyana Danilov,Stamatios Lerakis,Edgar Argulian,Jagat Narula,Girish N. Nadkarni,Benjamin S. Glicksberg +18 more
TL;DR: In this paper, a multi-center study was conducted with data from five New York City hospitals; four for internal testing and one serving as external validation, and the mean absolute error was 5.84% (5.82-5.85%) in internal testing, and 6.14% (6.13-6.16%) in external validation.
Journal ArticleDOI
Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram.
Akhil Vaid,Kipp W. Johnson,Marcus A. Badgeley,Sulaiman Somani,Mesude Bicak,Isotta Landi,Adam Russak,Shan Zhao,Matthew A. Levin,Robert S. Freeman,Alexander W. Charney,Atul Kukar,Bette Kim,Tatyana Danilov,Stamatios Lerakis,Edgar Argulian,Jagat Narula,Girish N. Nadkarni,Benjamin S. Glicksberg +18 more
TL;DR: In this paper, the authors developed DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population, and used them to develop a DL model capable of quantitatively quantifying the ventricular dysfunctions.